---
title: "Creating a Map Using ggplot2"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: pulse
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(pacman)
p_load(tidyverse)
```

A Simple World Map
===

Column {data-width=1000}
---
###

```{r}
map <- map_data("world")
ggplot(map, aes(x=long,y=lat,group=group))+geom_polygon(fill="lightblue",colour="white")+theme_void()
```

A Map for Specific Regions
===

###

```{r}
North_Asia <- c("China","Japan","Mongolia","North Korea","South Korea","Taiwan")
North_Asia_map <- map_data("world",region=North_Asia)
#Compute the centroid as the mean longitude and latitude
region.data<-North_Asia_map %>%
  group_by(region) %>%
  summarise(long=mean(long),lat=mean(lat)) %>%
  arrange()
#Ready to plot the map
ggplot(North_Asia_map,aes(x=long,y=lat))+geom_polygon(aes(group=group,fill=region))+geom_text(data=region.data,aes(label=region),size=5,hjust=0.5,col="#808080",fontface="bold")+scale_fill_viridis_d()+theme_void()+theme(legend.position="none")
```

A Chloropleth Map
===

Column {data-width=500}
---
###

```{r}
drinks<-read_csv("C:/Users/write/OneDrive/Desktop/school/data m&m/in-class labs/drinks.csv")
drinks_map <- drinks %>%
  left_join(map,by=c("country"="region"))
ggplot(drinks_map,aes(long,lat,group=group))+geom_polygon(aes(fill=total_litres_of_pure_alcohol),colour="white")+scale_fill_viridis_c(option="C")+labs(fill="Total Litres of Pure Alcohol")+theme_void()+theme(legend.position="bottom")


```

Column {data-width=500}
---
###

```{r}
ggplot(drinks,aes(map_id=country))+geom_map(aes(fill=total_litres_of_pure_alcohol),map=map,colour="white")+expand_limits(x=map$long,y=map$lat)+labs(fill="Total Litres of Pure Alcohol")+theme_void()+theme(legend.position = "bottom")
```

A US State-Level Map
===

###

```{r}
US_map <- map_data("state")
#Compute the centroid as the mean longitude and latitude
state_data<- US_map %>%
  filter(region != "district of columbia") %>%
  group_by(region) %>%
  summarise(long=mean(long),lat=mean(lat)) %>%
  arrange(region)
state_data$region.abb<-state.abb[-c(2,11)] #drop Alaska and Hawaii
p<- ggplot(US_map,aes(x=long,y=lat))+geom_polygon(aes(group=group,fill=region),colour="white")+geom_text(data=state_data,aes(label=region.abb),fontface="bold")+theme_void()+theme(legend.position="none")
p
```

A Dynamic Map Using Plotly - 1
===

###

```{r}
library(plotly)
ggplotly(p)
```

A Dynamic Map Using Plotly - 2
===

###

```{r}
US_map <- map_data("state")
#compute the centroid as the mean longitude and latitude
state_data <- US_map %>%
  filter(region!="district of columbia") %>%
  group_by(region) %>%
  summarise(long=mean(long),lat=mean(lat)) %>%
  arrange(region)
state_data$region.abb <- state.abb[-c(2,11)] #drop Alaska and Hawaii
crimes <- data.frame(region=rownames(USArrests),USArrests) %>% filter(region!=c("Alaska","Hawaii"))
crimes$region <- tolower(crimes$region)
crimes_map<-crimes %>% left_join(US_map, by="region")
g1<-ggplot(crimes_map,aes(x=long,y=lat)) +
  geom_polygon(aes(group=group,fill=Murder,text=paste0(region,":\n",Murder," murder arrests per 100,000"), colour="white")) + geom_text(data=state_data,aes(label=region.abb),fontface="bold",size=3)+scale_fill_viridis_c(option="C") +
  theme_void()

ggplotly(g1,tooltip="text")
```

A County-Level Map
===

###

```{r}
covid <- read.csv("C:/Users/write/OneDrive/Desktop/school/data m&m/in-class labs/COVID19.csv")
ohio<- covid %>%
  filter(administrative_area_level_2=="Ohio",date=="2021-12-31")
ohio <- ohio %>%
  rename(county=administrative_area_level_3)
ohio_county <- map_data("county",region="ohio")
ohio_county$subregion <- str_to_title(ohio_county$subregion)
ohio_map <- ohio %>%
  left_join(ohio_county,by=c("county"="subregion"))
g2<- ggplot(ohio_map,aes(x=long,y=lat))+
  geom_polygon(aes(group=group,fill=deaths,text=paste0("County: ",county,"\n",
                                                       "Total Deaths: ", deaths))) +
  geom_text(data=ohio,aes(x=longitude,y=latitude,label=county),
            color="white",fontface="bold")+
  scale_fill_viridis_c(option="H") + theme_minimal() + theme_void()
font <- list(family="Arial",size=15,color="white")
label <- list(bgcolor="#232F34",bordercolor="transparent",font=font)
ggplotly(g2,tooltip="text",width=850,height=800) %>%
  layout(font=font)
```